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Record W4400888470 · doi:10.1590/0102-311xen081624

(Re)criminalization of drug use: dynamics and perspectives

2024· editorial· en· W4400888470 on OpenAlexaboutno aff
Francisco Inácio Bastos

Bibliographic record

VenueCadernos de Saúde Pública · 2024
Typeeditorial
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCriminalizationDynamics (music)CriminologyPsychology

Abstract

fetched live from OpenAlex

Clearing up some misunderstandingsBefore addressing the (re)criminalization of drug use, we must clarify the distorted (re)emergence of certain points in the legal and scientific fields, including public health.Firstly, no country has breached international agreements concerning international trafficking and the illicit nature of certain psychoactive substances, called drugs 1 .Observed changes in drug policies conserve these Treaties.In national State policies, such as Portugal, possession for personal consumption is not criminalized, but it is subject to psychosocial intervention and, in case of recidivism, to noncriminal sanctions 2,3 .Changes in drug policies that, in addition to possession for personal consumption, involve the nature of markets refer to national legislation.Changes regarding the rules of use and the market are restricted to Cannabis and derivatives.Such is the case of differing policies such as those of US states and policies adopted in Uruguay and the Netherlands.We intend not to discuss their specificities, only to underline that none violates international treaties, none is formally ratified by the respective national States, and should not be understood as a supposed broad drug "liberation" or specifically of cannabis.Although local laws differ from each other, they all include clear rules which can be consulted in articles and reports, such as between member States of the European Community 4 .Below we will discuss the paradigmatic cases of the US state of Oregon, the province of British Columbia in Canada, and Brazil.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.182
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.305
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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